Coastal macroalgae worldwide provide multiple ecological functions and support vital ecosystem services. Thereby, it is important to monitor changes in the extent of benthic macroalgal cover. However, as in situ sampling is costly and time-consuming, areal estimates of macroalgal species cover are often based only on a limited number of samples. This low sampling effort likely yields very biased estimates, as macroalgal communities are often characterized by large spatial variability at multiple spatial scales. Moreover, ecological time series are often short-term, making it impossible to assess changes in algal communities over decades and relate this to different human pressures and/or climate change. The Landsat series satellites have operated for 40 years. In the current study, we tested if the Landsat sensors could be used for mapping the cover of shallow water benthic macroalgae. This study was carried out at two sites in the West Estonian Archipelago, in the northeastern Baltic Sea. Our results show that the Landsat imagery accurately reflected both spatial and temporal variability in benthic algal cover. To conclude, the current methodology can be used to improve the existing assessments of areal macroalgal cover, or to estimate the cover values, in areas and times lacking ecological observations.

Remote sensing is a valuable tool for surveying submerged aquatic vegetation (SAV) distribution patterns at extensive spatial and temporal scales. Only regular mapping over successive time periods (e.g., months, years) allows for a quantitative assessment of SAV loss or recolonization extent. Still, there are only a limited number of studies assessing temporal changes in SAV patterns. ESA Sentinel-2 (S2) has a high revisiting frequency permitting the multi-temporal assessment of SAV dynamics both seasonally and inter-annually. In the current study, a physics-based IDA (Image Data Analysis) model was used for the reconstruction of past SAV percent cover (%cover) patterns in the Baltic Sea coastal waters based on S2 archived images. First, we aimed at capturing and quantifying intra-annual spatiotemporal SAV dynamics happening during a growing season. Modeling results showed that significant changes took place in SAV %cover: the extent of low-cover (0–30% coverage) and intermediate-cover (30–70% coverage) areas decreased, while high-cover (70–100% coverage) areas increased during the growing period. Secondly, we also aimed at detecting SAV %cover spatiotemporal variations inter-annually (over the years 2016–2022). Inter-annual variability in %cover patterns was greater in the beginning of the vegetation period (May). The peak of the growing period (July/August) showed greater stability in the areal extent of the %cover classes.

Accurate determination of the water depth and benthic macroalgae composition in coastal and inland water bodies is important due to the high commercial and ecological value of these regions. Benthic habitat mapping by conventional methods provides good accuracy, but these methods are very expensive and limited by manpower and time factor, which is necessary for mapping large areas. Remote sensing methods significantly complement contact measurements and give additional information about the hard-to-reach areas. The usefulness of free Sentinel-2 data in bathymetry and habitat mapping has been demonstrated in clear oceanic waters. The aim of this study was to further test the suitability of Sentinel-2 imagery in creating maps of dominant benthic types, as well as in estimating bathymetry in optically complex marine and lake waters. Two study sites were selected to cover a representative range of optical variability - Lake Garda in northern Italy (an intermediate between clear ocean and optically very complex waters) and Viimsi peninsula on the Estonian side of the Gulf of Finland, in the Baltic Sea. The results show that Sentinel-2 imagery with 10 m spatial resolution is suitable for bathymetry and habitat mapping in optically complex inland and coastal waters. Our results show that bathymetry mapping is sufficiently accurate in waters less than 4 m deep in the case of the Baltic Sea and up to 7 m deep in Lake Garda. In such depths, the R² was above 0.93 in all four Sentinel-2 images used in the study. Bottom type mapping accuracy was in all cases over 73%, which is considered to be good, but due to the limited number of sampling points in both test sites, further studies are needed. The Sentinel-2 data quality and no cost of the imagery for users make it very useful for mapping bathymetry and shallow water habitats over large coastal areas or high number of lakes, especially in hard to reach by in situ methods areas. Moreover, the frequent revisit time allows moving from one-off maps to monitoring of temporal changes happening in dynamic shallow inland and coastal waters.

This study investigated the potential to predict primary production in benthic ecosystems using meteorological variables and spectral indices. In situ production experiments were carried out during the vegetation season of 2020, wherein the primary production and spectral reflectance of different communities of submerged aquatic vegetation (SAV) were measured and chlorophyll (Chl a+b) concentration was quantified in the laboratory. The reflectance of SAV was measured both in air and underwater. First, in situ reflectance spectra of each SAV class were used to calculate different spectral indices, and then the indices were correlated with Chl a+b. Indices using red and blue band combinations such as 650/450 and 650/480 nm explained the largest part of variability in Chl a+b for datasets measured in air and underwater. Subsequently, the best-performing indices were used in boosted regression trees (BRT) models, together with meteorological data to predict the community photosynthesis of different SAV classes. The predictive power (R²) of production models were very high, estimated at the range of 0.82–0.87. The variable contributing the most to the model description was SAV class, followed in most cases by the water temperature. Nevertheless, the inclusion of spectral indices significantly improved BRT models, often by over 20%, and surprisingly their contribution mostly exceeded that of photosynthetically active radiation.